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Articles 1 - 4 of 4
Full-Text Articles in Social and Behavioral Sciences
Attention-Based Knowledge Tracing With Heterogeneous Information Network Embedding, Nan Zhang, Ye Du, Ke Deng, Li Li, Jun Shen, Geng Sun
Attention-Based Knowledge Tracing With Heterogeneous Information Network Embedding, Nan Zhang, Ye Du, Ke Deng, Li Li, Jun Shen, Geng Sun
Faculty of Engineering and Information Sciences - Papers: Part B
Knowledge tracing is a key area of research contributing to personalized education. In recent times, deep knowledge tracing has achieved great success. However, the sparsity of students’ practice data still limits the performance and application of knowledge tracing. An additional complication is that the contribution of the answer record to the current knowledge state is different at each time step. To solve these problems, we propose Attention-based Knowledge Tracing with Heterogeneous Information Network Embedding (AKTHE). First, we describe questions and their attributes with a heterogeneous information network and generate meaningful node embeddings. Second, we capture the relevance of historical data …
Developing An Ontology For Representing The Domain Knowledge Specific To Non-Pharmacological Treatment For Agitation In Dementia, Zhenyu Zhang, Ping Yu, H.C. Chang, S K. Lau, Cui Tao, Ning Wang, Mengyang Yin, Chao Deng
Developing An Ontology For Representing The Domain Knowledge Specific To Non-Pharmacological Treatment For Agitation In Dementia, Zhenyu Zhang, Ping Yu, H.C. Chang, S K. Lau, Cui Tao, Ning Wang, Mengyang Yin, Chao Deng
Faculty of Engineering and Information Sciences - Papers: Part B
Introduction: A large volume of clinical care data has been generated for managing agitation in dementia. However, the valuable information in these data has not been used effectively to generate insights for improving the quality of care. Application of artificial intelligence technologies offers us enormous opportunities to reuse these data. For health data science to achieve this, this study focuses on using ontology to coding clinical knowledge for non-pharmacological treatment of agitation in a machine-readable format. Methods: The resultant ontology—Dementia-Related Agitation Non-Pharmacological Treatment Ontology (DRANPTO)—was developed using a method adopted from the NeOn methodology. Results: DRANPTO consisted of 569 concepts …
Towards An Assessment Framework Of Reuse: A Knowledge Level Analysis Approach, Ghassan Beydoun, Achim Hoffmann, Rafael Valencia-Garcia, Jun Shen, Asifqumer Gill
Towards An Assessment Framework Of Reuse: A Knowledge Level Analysis Approach, Ghassan Beydoun, Achim Hoffmann, Rafael Valencia-Garcia, Jun Shen, Asifqumer Gill
Faculty of Engineering and Information Sciences - Papers: Part B
The process of assessing the suitability of reuse of a software component is complex. Indeed, software systems are typically developed as an assembly of existing components. The complexity of the assessment process is due to lack of clarity on how to compare the cost of adaptation of an existing component versus the cost of developing it from scratch. Indeed, often pursuit of reuse can lead to excessive rework and adaptation, or developing suites of components that often get neglected. This paper is an important step towards modelling the complex reuse assessment process. To assess the success factors that can underpin …
Towards Massive Data And Sparse Data In Adaptive Micro Open Educational Resource Recommendation: A Study On Semantic Knowledge Base Construction And Cold Start Problem, Geng Sun, Tingru Cui, Ghassan Beydoun, Shiping Chen, Fang Dong, Dongming Xu, Jun Shen
Towards Massive Data And Sparse Data In Adaptive Micro Open Educational Resource Recommendation: A Study On Semantic Knowledge Base Construction And Cold Start Problem, Geng Sun, Tingru Cui, Ghassan Beydoun, Shiping Chen, Fang Dong, Dongming Xu, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part B
Micro Learning through open educational resources (OERs) is becoming increasingly popular. However, adaptive micro learning support remains inadequate by current OER platforms. To address this, our smart system, Micro Learning as a Service (MLaaS), aims to deliver personalized OER with micro learning to satisfy their real-time needs.